Tootfinder

CMP: A Composable Meta Prompt for SAM-Based Cross-Domain Few-Shot Segmentation
Shuai Chen, Fanman Meng, Chunjin Yang, Haoran Wei, Chenhao Wu, Qingbo Wu, Hongliang Li
https://arxiv.org/abs/2507.16753

CMP: A Composable Meta Prompt for SAM-Based Cross-Domain Few-Shot Segmentation
Cross-Domain Few-Shot Segmentation (CD-FSS) remains challenging due to limited data and domain shifts. Recent foundation models like the Segment Anything Model (SAM) have shown remarkable zero-shot generalization capability in general segmentation tasks, making it a promising solution for few-shot scenarios. However, adapting SAM to CD-FSS faces two critical challenges: reliance on manual prompt and limited cross-domain ability. Therefore, we propose the Composable Meta-Prompt (CMP) framework t…

CyberArmor has a post up detailing abuse of the Vercel app development platform. Vercel is a legit tool that allows for the rapid creation of web applications. They have a free tier and unfortunately it gets abused. The free tier apps use the domain pattern *.vercel.app. I recommend blocking this domain and only allowing access to vetted sites using this domain name. Note that commercial sites using the Vercel platform will have their own domain names so your potential for collateral damage …

Massless monopole-string-domain wall fermions and polyhedral vacuum fermions
Minoru Eto, Yuito Suzuki
https://arxiv.org/abs/2506.16765 https://

Massless monopole-string-domain wall fermions and polyhedral vacuum fermions
Fermion zero modes of Bogomol'nyi-Prasad-Sommerfield monopole-string-domain wall composites in three spatial dimensions are studied. We analytically solve the Dirac equation and prove the existence of one fermion zero mode. Depending on mass parameters of bosons/fermions in the model, the zero modes are localized either on the monopoles, strings or domain walls, which we call monopole-string-domain wall fermions. We also show that in special cases, the zero modes can be confined within a finite…

I chose a new home for my blogging and articles:
#DigitalMigration

Migrating Stuff
changing the place I blog, post articles and...stuff So I've chosen stuff.davidaugust.com as my new custom domain for this site. Setting up a custom domain with ghost, the software running this site, is pretty straightforward. I did it a few minutes ago and already can now access

Semantic Scaffolding: Augmenting Textual Structures with Domain-Specific Groupings for Accessible Data Exploration
Jonathan Zong, Isabella Pedraza Pineros, Mengzhu Katie Chen, Daniel Hajas, Arvind Satyanarayan
https://arxiv.org/abs/2506.15883

Semantic Scaffolding: Augmenting Textual Structures with Domain-Specific Groupings for Accessible Data Exploration
Drawing connections between interesting groupings of data and their real-world meaning is an important, yet difficult, part of encountering a new dataset. A lay reader might see an interesting visual pattern in a chart but lack the domain expertise to explain its meaning. Or, a reader might be familiar with a real-world concept but struggle to express it in terms of a dataset's fields. In response, we developed semantic scaffolding, a technique for using domain-specific information from large l…

Inductive Domain Transfer In Misspecified Simulation-Based Inference
Ortal Senouf, Antoine Wehenkel, C\'edric Vincent-Cuaz, Emmanuel Abb\'e, Pascal Frossard
https://arxiv.org/abs/2508.15593

Inductive Domain Transfer In Misspecified Simulation-Based Inference
Simulation-based inference (SBI) is a statistical inference approach for estimating latent parameters of a physical system when the likelihood is intractable but simulations are available. In practice, SBI is often hindered by model misspecification--the mismatch between simulated and real-world observations caused by inherent modeling simplifications. RoPE, a recent SBI approach, addresses this challenge through a two-stage domain transfer process that combines semi-supervised calibration with…

DoSReMC: Domain Shift Resilient Mammography Classification using Batch Normalization Adaptation
U\u{g}urcan Aky\"uz, Deniz Katircioglu-\"Ozt\"urk, Emre K. S\"usl\"u, Burhan Kele\c{s}, Mete C. Kaya, Gamze Durhan, Meltem G. Akp{\i}nar, Figen B. Demirkaz{\i}k, G\"ozde B. Akar
https://arxiv.org/abs/2508.15452

DoSReMC: Domain Shift Resilient Mammography Classification using Batch Normalization Adaptation
Numerous deep learning-based solutions have been developed for the automatic recognition of breast cancer using mammography images. However, their performance often declines when applied to data from different domains, primarily due to domain shift - the variation in data distributions between source and target domains. This performance drop limits the safe and equitable deployment of AI in real-world clinical settings. In this study, we present DoSReMC (Domain Shift Resilient Mammography Class…

WangchanThaiInstruct: An instruction-following Dataset for Culture-Aware, Multitask, and Multi-domain Evaluation in Thai
Peerat Limkonchotiwat, Pume Tuchinda, Lalita Lowphansirikul, Surapon Nonesung, Panuthep Tasawong, Alham Fikri Aji, Can Udomcharoenchaikit, Sarana Nutanong
https://arxiv.org/abs/2508.15239…

WangchanThaiInstruct: An instruction-following Dataset for Culture-Aware, Multitask, and Multi-domain Evaluation in Thai
Large language models excel at instruction-following in English, but their performance in low-resource languages like Thai remains underexplored. Existing benchmarks often rely on translations, missing cultural and domain-specific nuances needed for real-world use. We present WangchanThaiInstruct, a human-authored Thai dataset for evaluation and instruction tuning, covering four professional domains and seven task types. Created through a multi-stage quality control process with annotators, dom…

Discrete Radar based on Modulo Arithmetic
Nishant Mehrotra, Sandesh Rao Mattu, Saif Khan Mohammed, Ronny Hadani, Robert Calderbank
https://arxiv.org/abs/2508.15671 https://

Discrete Radar based on Modulo Arithmetic
Zak-OTFS is modulation scheme where signals are formed in the delay-Doppler (DD) domain, converted to the time domain (DD) for transmission and reception, then returned to the DD domain for processing. We describe how to use the same architecture for radar sensing. The intended delay resolution is $\frac{1}{B}$ where $B$ is the radar bandwidth, and the intended Doppler resolution is $\frac{1}{T}$ where $T$ is the transmission time. We form a radar waveform in the DD domain, illuminate the scatt…

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling
Vladyslav Bulhakov, Giordano d'Aloisio, Claudio Di Sipio, Antinisca Di Marco, Davide Di Ruscio
https://arxiv.org/abs/2507.14735

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling
The introduction of large language models (LLMs) has enhanced automation in software engineering tasks, including in Model Driven Engineering (MDE). However, using general-purpose LLMs for domain modeling has its limitations. One approach is to adopt fine-tuned models, but this requires significant computational resources and can lead to issues like catastrophic forgetting. This paper explores how hyperparameter tuning and prompt engineering can improve the accuracy of the Llama 3.1 model for…

General-domain FC-based shock-dynamics solver I: Basic elements
Oscar P. Bruno, Daniel V. Leibovici
https://arxiv.org/abs/2506.16076 https://

General-domain FC-based shock-dynamics solver I: Basic elements
This contribution, Part I in a two-part article series, presents a general-domain version of the FC-SDNN (Fourier Continuation Shock-detecting Neural Network) spectral scheme for the numerical solution of nonlinear conservation laws, which is applicable under arbitrary boundary conditions and in general domains. Like the previous simple-domain contribution (Journal of Computational Physics X 15, (2022)), the present approach relies on the use of the Fourier Continuation method for accurate spec…

Origin of Suppressed Ferroelectricity in k-Ga$_2$O$_3$: Interplay Between Polarization and Lattice Domain Walls
Yonghao Zhu, Zhi Wang, Junwei Luo, Lin-Wang Wang
https://arxiv.org/abs/2507.16167

Origin of Suppressed Ferroelectricity in k-Ga$_2$O$_3$: Interplay Between Polarization and Lattice Domain Walls
The large discrepancy between experimental and theoretical remanent polarization and coercive field limits the applications of wide-band-gap ferroelectric materials. Here, using a machine-learning potential trained on ab-initio molecular dynamics data, we identify a new mechanism of the interplay between polarization domain wall (PDW) and lattice domain wall (LDW) in ferroelectric k-phase gallium oxide (Ga2O3), which reconciles predictions with experimental observations. Our results reveal that…

Dora: A Controller Provisioning Strategy in Hierarchical Domain-based Satellite Networks
Qiyuan Peng, Qi Zhang, Yue Gao, Kun Qiu
https://arxiv.org/abs/2507.14512

Dora: A Controller Provisioning Strategy in Hierarchical Domain-based Satellite Networks
The rapid proliferation of satellite constellations in Space-Air-Ground Integrated Networks (SAGIN) presents significant challenges for network management. Conventional flat network architectures struggle with synchronization and data transmission across massive distributed nodes. In response, hierarchical domain-based satellite network architectures have emerged as a scalable solution, highlighting the critical importance of controller provisioning strategies. However, existing network managem…

TIL in 2017 the UK Payments Administration renamed themselves "Pay.UK" but have not obtained the domain registration for pay.uk
(This isn't a "forgot to register" situation - open .uk registration between 2014 and 2019 was reserved for equivalent co.uk holders. The domain was registered by the holder two weeks before it would have been available.)

The effect of fiber plasticity on domain formation in soft biological composites -- Part II: An imperfection analysis
Dimitris Iordanidis, Fernanda F. Fontenele, Konstantinos Poulios, Michalis Agoras, Nikolaos Bouklas
https://arxiv.org/abs/2507.14764

The effect of fiber plasticity on domain formation in soft biological composites -- Part II: An imperfection analysis
The main objective of this work is to numerically investigate the effect of geometric imperfections on the macroscopic response and domain formation in soft biological composites that exhibit plasticity in the stiff (fiber) phase.This work builds on the corresponding bifurcation analysis in Part I of this study for simple laminates with perfectly flat layers under plane strain, nonmonotonic loading conditions, aligned with the layer direction. The post-bifurcation solution obtained in Part I fo…

"I do not need the one magic machine that claims to solve all my issues and then makes me jump through conversational hoops to get a mediocre result. That is actually the opposite of what I need."
On chatbots as a bad design paradigm
(Original title: “ChatBot” is bad design)
https://t…

"ChatBot" is bad design
As anyone who has ever seen anything I have created knows: I am not a designer. So take my reasoning and thinking here with a grain of salt. I do have many designers amongst my friends and I do think that a lot of software engineering falls within the domain of design to a certain […]

Last passage percolation in lower triangular domain
Dan Betea, Anton Nazarov, Pavel Nikitin
https://arxiv.org/abs/2507.16320 https://…

Last passage percolation in lower triangular domain
Last passage percolation (LPP) in an $n\times n$ lower triangular domain has nice connections with various generalizations of Schur measures. LPP along an anti-diagonal, from $(1,n)$ to $(n,1)$, gives a distribution of a highest column of a random composition with respect to a Demazure measure (a non-symmetric analog of a Schur measure). LPP along a main diagonal, from $(1,1)$ to $(n,n)$, is distributed as a marginal of a Pfaffian Schur process. In the first case we show that the asymptotics fo…

Identifying Algorithmic and Domain-Specific Bias in Parliamentary Debate Summarisation
Eoghan Cunningham, James Cross, Derek Greene
https://arxiv.org/abs/2507.14221

Identifying Algorithmic and Domain-Specific Bias in Parliamentary Debate Summarisation
The automated summarisation of parliamentary debates using large language models (LLMs) offers a promising way to make complex legislative discourse more accessible to the public. However, such summaries must not only be accurate and concise but also equitably represent the views and contributions of all speakers. This paper explores the use of LLMs to summarise plenary debates from the European Parliament and investigates the algorithmic and representational biases that emerge in this context.…

Advancing Lunar Communication through Inter-domain Space Networks and Dynamic Orchestration
Selen Gecgel Cetin, Baris Donmez, Gunes Karabulut Kurt
https://arxiv.org/abs/2507.15483

Advancing Lunar Communication through Inter-domain Space Networks and Dynamic Orchestration
The reawakened era of lunar exploration is defined by a strategic shift from temporary visits to a sustained international and commercial presence, resulting in an unprecedented demand for a robust and continuously available communication infrastructure. The conventional direct-to-Earth communication architecture relies on limited and oversubscribed deep space networks, which are further challenged by the radiative environment and insufficient visibility in certain areas of the cislunar domain.…

Bottom-up Domain-specific Superintelligence: A Reliable Knowledge Graph is What We Need
Bhishma Dedhia, Yuval Kansal, Niraj K. Jha
https://arxiv.org/abs/2507.13966

Bottom-up Domain-specific Superintelligence: A Reliable Knowledge Graph is What We Need
Language models traditionally used for cross-domain generalization have recently demonstrated task-specific reasoning. However, their top-down training approach on general corpora is insufficient for acquiring abstractions needed for deep domain expertise. This may require a bottom-up approach that acquires expertise by learning to compose simple domain concepts into more complex ones. A knowledge graph (KG) provides this compositional structure, where domain primitives are represented as head-…

"only 3 percent of the documents that were given to us were new. 97 percent were already in the public domain and less than 1 percent of the Epstein files have actually been released. “
'Let me break some news': Dem lawmaker pours cold water on new Epstein file release - Raw Story
https://www.rawstory.com/epstein-files-khanna/

At the "wait five days for your domain name to transfer" stage of things. Which is coupled with "your domain name will stop working the moment it finally transfers because you screwed up the order of operations". No big deal in this case but I can't help but think all of this could work better.
(I'm trying out Porkbun on a test domain, to compare with Namecheap.)

Ist hoffentlich nur so für den Prototypen geplant ... für die Produktion scheint mir ein URI unter einer github-Domain keine gute Idee ... (aus der ODIS-LOD-Studie)

Implementing and Verifying a Fourier Domain Approach to Fast Stochastic X-ray Polarimetry Timing
Melissa Ewing, Adam Ingram, John Rankin, Fabio Muleri
https://arxiv.org/abs/2507.15461

Implementing and Verifying a Fourier Domain Approach to Fast Stochastic X-ray Polarimetry Timing
The launch of the Imaging X-ray Polarimetry Explorer (IXPE), the first space-based polarimeter since 1978, offers a two order of magnitude improvement to the measurement of X-ray polarisation than its predecessor OSO-8, offering unprecedented precision for the measurement of polarisation degree and polarisation angle of X-ray sources. This advancement lends itself to the birth of a number of contemporary techniques to study Galactic compact objects, including X-ray polarimetry-timing, the study…

How to download videos from Vimeo, even the paywalled ones
#Vimeo

Generalized Eshelby's inclusion and inhomogeneity problems for transient heat transfer
Chunlin Wu, Zhenhua Wei, Huiming Yin
https://arxiv.org/abs/2506.16498

Generalized Eshelby's inclusion and inhomogeneity problems for transient heat transfer
Eshelby's problems have been generalized to arbitrary shape of polygonal, polyhedral, and ellipsoidal inclusions embedded in an infinite isotropic domain under transient heat transfer, and Eshelby's tensors have been analytically derived to evaluate disturbed thermal fields caused by inclusions with a polynomial-form eigen-fields. Transformed coordinates are applied to arbitrarily shaped inclusions for domain integrals of transient fundamental solutions. This formulation is for general transien…

I use a Cloudflare tunnel which, among other things, requires a 2FA code. A code number that I was not receiving... Why? Because I'm so stupid that I was using the old Proton Mail domain (protonmail.com) instead of the new one (proton.me), not remembering that my Cloudflare account was associated with the latter.
I am the weakest link in my own chain.

Partition function for several Ising model interface structures
Alessio Squarcini, Piotr Nowakowski, Douglas B. Abraham, Anna Macio{\l}ek
https://arxiv.org/abs/2506.17170

Partition function for several Ising model interface structures
We employ a procedure that enables us to calculate the excess free energies for a finite Ising cylinder with domain walls analytically. This procedure transparently covers all possible configurations of the domain walls under given boundary conditions and allows for a physical interpretation in terms of coarse-grained quantities such as surface and point tensions. The resulting integrals contain all the information about finite-size effects; we extract them by careful asymptotic analysis using …

D2Diff : A Dual Domain Diffusion Model for Accurate Multi-Contrast MRI Synthesis
Sanuwani Dayarathna, Himashi Peiris, Kh Tohidul Islam, Tien-Tsin Wong, Zhaolin Chen
https://arxiv.org/abs/2506.15750

D2Diff : A Dual Domain Diffusion Model for Accurate Multi-Contrast MRI Synthesis
Multi contrast MRI synthesis is inherently challenging due to the complex and nonlinear relationships among different contrasts. Each MRI contrast highlights unique tissue properties, but their complementary information is difficult to exploit due to variations in intensity distributions and contrast specific textures. Existing methods for multi contrast MRI synthesis primarily utilize spatial domain features, which capture localized anatomical structures but struggle to model global intensity …

Broadband coherent Raman spectroscopy based on single-pulse spectral-domain ghost imaging
Jing Hu, Tianjian Lv, Zhaoyang Wen, Wending Huang, Ming Yan, Heping Zeng
https://arxiv.org/abs/2507.16161

Broadband coherent Raman spectroscopy based on single-pulse spectral-domain ghost imaging
Broadband coherent anti-Stokes Raman scattering (CARS) spectroscopy plays a vital role in chemical sensing and label-free vibrational imaging, yet conventional methods suffer from limited acquisition speeds and complex detection schemes. Here, we demonstrate high-speed broadband CARS enabled by nonlinear spectral ghost imaging combined with time-stretch dispersive Fourier-transform spectroscopy (TS-DFT). We exploit modulation instability to generate a stochastic supercontinuum as the Stokes sou…

Enhancing Domain Diversity in Synthetic Data Face Recognition with Dataset Fusion
Anjith George, Sebastien Marcel
https://arxiv.org/abs/2507.16790 https://…

Enhancing Domain Diversity in Synthetic Data Face Recognition with Dataset Fusion
While the accuracy of face recognition systems has improved significantly in recent years, the datasets used to train these models are often collected through web crawling without the explicit consent of users, raising ethical and privacy concerns. To address this, many recent approaches have explored the use of synthetic data for training face recognition models. However, these models typically underperform compared to those trained on real-world data. A common limitation is that a single gene…

A Novel Domain-Aware CNN Architecture for Faster-than-Nyquist Signaling Detection
Osman Tokluoglu, Enver Cavus, Ebrahim Bedeer, Halim Yanikomeroglu
https://arxiv.org/abs/2507.15291

A Novel Domain-Aware CNN Architecture for Faster-than-Nyquist Signaling Detection
This paper proposes a convolutional neural network (CNN)-based detector for faster-than-Nyquist (FTN) signaling that employs structured fixed kernel layers with domain-informed masking to mitigate intersymbol interference (ISI). Unlike standard CNNs with sliding kernels, the proposed method utilizes fixed-position kernels to directly capture ISI effects at varying distances from the central symbol. A hierarchical filter allocation strategy is also introduced, assigning more filters to earlier l…

Second-order boundary estimates for solutions to a class of quasilinear elliptic equations
Giuseppe Spadaro, Domenico Vuono
https://arxiv.org/abs/2507.16402

Second-order boundary estimates for solutions to a class of quasilinear elliptic equations
We prove global second-order regularity for a class of quasilinear elliptic equations, both with homogeneous Dirichlet and Neumann boundary conditions. A condition on the integrability of the second fundamental form on the boundary of the domain is required. As a consequence, with the additional assumption that the source term has a sign, we obtain integrability properties of the inverse of the gradient of the solution. Assuming convexity of the domain, no boundary regularity is required.

A condition equivalent to the H\"{o}lder continuity of harmonic functions on unbounded Lipschitz domains
Marijan Markovic
https://arxiv.org/abs/2507.14511

A condition equivalent to the Hölder continuity of harmonic functions on unbounded Lipschitz domains
Our main result concerns the behavior of bounded harmonic functions on a domain in $\mathbb{R}^N$ which may be represented as a strict epigraph of a Lipschitz function on $\mathbb{R}^{N-1}$. Generally speaking, the result says that the Hölder continuity of a harmonic function on such a domain is equivalent to the uniform Hölder continuity along the straight lines determined by the vector $\mathbf{e}_N$, where $\mathbf{e}_1,\mathbf{e}_2,\dots,\mathbf {e}_N$ is the base of standard vectors in $…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/5]:
- Foundation Models for Cross-Domain EEG Analysis Application: A Survey
Hongqi Li, Yitong Chen, Yujuan Wang, Weihang Ni, Haodong Zhang

interactome_pdz: PDZ-domain interactome (2005)
A network of PDZ-domain-mediated protein–protein binding interactions, extracted from the PDZBase database. Nodes represent proteins and an edge represents a binding interaction between two proteins.
This network has 212 nodes and 244 edges.
Tags: Biological, Protein interactions, Unweighted

Comparison of substructured non-overlapping domain decomposition and overlapping additive Schwarz methods for large-scale Helmholtz problems with multiple sources
Boris Martin, Pierre Jolivet, Christophe Geuzaine
https://arxiv.org/abs/2506.16875

Comparison of substructured non-overlapping domain decomposition and overlapping additive Schwarz methods for large-scale Helmholtz problems with multiple sources
Solving large-scale Helmholtz problems discretized with high-order finite elements is notoriously difficult, especially in 3D where direct factorization of the system matrix is very expensive and memory demanding, and robust convergence of iterative methods is difficult to obtain. Domain decomposition methods (DDM) constitute one of the most promising strategy so far, by combining direct and iterative approaches: using direct solvers on overlapping or non-overlapping subdomains, as a preconditi…

Foundation Models for Cross-Domain EEG Analysis Application: A Survey
Hongqi Li, Yitong Chen, Yujuan Wang, Weihang Ni, Haodong Zhang
https://arxiv.org/abs/2508.15716 https://

Foundation Models for Cross-Domain EEG Analysis Application: A Survey
Electroencephalography (EEG) analysis stands at the forefront of neuroscience and artificial intelligence research, where foundation models are reshaping the traditional EEG analysis paradigm by leveraging their powerful representational capacity and cross-modal generalization. However, the rapid proliferation of these techniques has led to a fragmented research landscape, characterized by diverse model roles, inconsistent architectures, and a lack of systematic categorization. To bridge this g…

Homomorphism Calculus for User-Defined Aggregations
Ziteng Wang, Ruijie Fang, Linus Zheng, Dixin Tang, Isil Dillig
https://arxiv.org/abs/2508.15109 https://

Homomorphism Calculus for User-Defined Aggregations
Data processing frameworks like Apache Spark and Flink provide built-in support for user-defined aggregation functions (UDAFs), enabling the integration of domain-specific logic. However, for these frameworks to support \emph{efficient} UDAF execution, the function needs to satisfy a \emph{homomorphism property}, which ensures that partial results from independent computations can be merged correctly. Motivated by this problem, this paper introduces a novel \emph{homomorphism calculus} that can…

Generative Multi-Target Cross-Domain Recommendation
Jinqiu Jin, Yang Zhang, Junwei Pan, Fuli Feng, Hua Lu, Haijie Gu, Xiangnan He
https://arxiv.org/abs/2507.12871

Generative Multi-Target Cross-Domain Recommendation
Recently, there has been a surge of interest in Multi-Target Cross-Domain Recommendation (MTCDR), which aims to enhance recommendation performance across multiple domains simultaneously. Existing MTCDR methods primarily rely on domain-shared entities (\eg users or items) to fuse and transfer cross-domain knowledge, which may be unavailable in non-overlapped recommendation scenarios. Some studies model user preferences and item features as domain-sharable semantic representations, which can be u…

Experimental Setup and Software Pipeline to Evaluate Optimization based Autonomous Multi-Robot Search Algorithms
Aditya Bhatt, Mary Katherine Corra, Franklin Merlo, Prajit KrisshnaKumar, Souma Chowdhury
https://arxiv.org/abs/2506.16710

Experimental Setup and Software Pipeline to Evaluate Optimization based Autonomous Multi-Robot Search Algorithms
Signal source localization has been a problem of interest in the multi-robot systems domain given its applications in search \& rescue and hazard localization in various industrial and outdoor settings. A variety of multi-robot search algorithms exist that usually formulate and solve the associated autonomous motion planning problem as a heuristic model-free or belief model-based optimization process. Most of these algorithms however remains tested only in simulation, thereby losing the opportu…

The effects of no-slip boundaries and external force torque on two-dimensional turbulence in a square domain
Alisa Shikanian, Vladimir Parfenyev
https://arxiv.org/abs/2508.13590

The effects of no-slip boundaries and external force torque on two-dimensional turbulence in a square domain
We study two-dimensional turbulence in a square no-slip domain without bottom drag using direct numerical simulations. The dynamics are shown to depend strongly on the torque $M$ of the external forcing. When $M$ is relatively large, a long-lived coherent vortex forms at the domain center, establishing a persistent angular momentum. At lower torques, the angular momentum undergoes random sign reversals due to spontaneous switching of the central vortex circulation, though it predominantly align…

Heute vor 63 Jahren: Am 22. August 1962 teste die #Sowjetunion im Atomtestgebiet Nowaja Semlja eine #Atombombe mit der Testnummer 154 (1.6 Mt, atmosphärisch).

Doing a more extensive SI/PI workup on the switch line card in preparation for a) a blog update on the switch project and b) getting all my I's dotted and T's crossed before spending a lot of money stuffing three more boards and ordering the logic board.
Here's the 2.5V rail, putting out a bit over 2A at 2.5V. Ripple is dominated by ~300 kHz switching noise plus some harmonics of the 125 MHz PHY symbol rate (which makes sense this rail is driving the PHY PAM5 transceivers a…

SFNet: A Spatio-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis
Xinyue Yang, Meiliang Liu, Yunfang Xu, Xiaoxiao Yang, Zhengye Si, Zijin Li, Zhiwen Zhao
https://arxiv.org/abs/2507.16267

SFNet: A Spatio-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that predominantly affects the elderly population and currently has no cure. Magnetic Resonance Imaging (MRI), as a non-invasive imaging technique, is essential for the early diagnosis of AD. MRI inherently contains both spatial and frequency information, as raw signals are acquired in the frequency domain and reconstructed into spatial images via the Fourier transform. However, most existing AD diagnostic models extract featu…

AUTOPSY: A Framework for Tackling Privacy Challenges in the Automotive Industry
Sebastian Pape, Anis Bkakria, Maurice Heymann, Badreddine Chah, Abdeljalil Abbas-Turki, Sarah Syed-Winkler, Matthias Hiller, Reda Yaich
https://arxiv.org/abs/2507.16788

AUTOPSY: A Framework for Tackling Privacy Challenges in the Automotive Industry
With the General Data Protection Regulation (GDPR) in place, all domains have to ensure compliance with privacy legislation. However, compliance does not necessarily result in a privacy-friendly system as for example getting users' consent to process their data does not improve the privacy-friendliness of the system. Therefore, the goal of the AUTOPSY project was to support the privacy engineering process in the automotive domain by providing several building blocks which technically improve th…

Task-Generalized Adaptive Cross-Domain Learning for Multimodal Image Fusion
Mengyu Wang, Zhenyu Liu, Kun Li, Yu Wang, Yuwei Wang, Yanyan Wei, Fei Wang
https://arxiv.org/abs/2508.15505

Task-Generalized Adaptive Cross-Domain Learning for Multimodal Image Fusion
Multimodal Image Fusion (MMIF) aims to integrate complementary information from different imaging modalities to overcome the limitations of individual sensors. It enhances image quality and facilitates downstream applications such as remote sensing, medical diagnostics, and robotics. Despite significant advancements, current MMIF methods still face challenges such as modality misalignment, high-frequency detail destruction, and task-specific limitations. To address these challenges, we propose …

Milnor fibrations and oriented matroids
Paul M\"ucksch, Masahiko Yoshinaga
https://arxiv.org/abs/2508.15331 https://arxiv.org/pdf/2508.15331

Milnor fibrations and oriented matroids
We introduce a combinatorial model for the Milnor fibration of a complexified real arrangement using oriented matroids. It is a poset quasi-fibration, a notion recently introduced by the first author, whose domain is a subdivision of the Salvetti complex stemming from a natural subdivision of the dual oriented matroid complex. This yields a concrete finite regular CW complex which is homotopy equivalent to the Milnor fiber of the complexified real arrangement and implies that the homotopy type …

Crosslisted article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/1]:
- Non-overlapping, Schwarz-type Domain Decomposition Method for Physics and Equality Constrained Ar...
Qifeng Hu, Shamsulhaq Basir, Inanc Senocak

@… @… Most password managers will only fill in a password automatically when on the appropriate domain. So this implies that the user has gone to the correct site… and is then being clickjacked by the site owner? Or is the id…

Pseudospectrum and time-domain analysis of the EFT corrected black holes
Li-Ming Cao, Ming-Fei Ji, Liang-Bi Wu, Yu-Sen Zhou
https://arxiv.org/abs/2508.13894 https://

Pseudospectrum and time-domain analysis of the EFT corrected black holes
We study the linear perturbations of a spherically symmetric black hole corrected by dimension-6 terms in EFT of gravity. The solution is asymptotically flat and characterized by two parameters -- a mass parameter $M$ and a dimensionless parameter $\varepsilon$ related to the EFT length scale $l$, and the perturbation equation incorporates a velocity factor which is not constant. The quasinormal modes (QNMs) and time-domain waveforms are studied within the hyperboloidal framework. This approach…

The Bode Plots for Sliding-Mode Control Design
Ulises P\'erez-Ventura
https://arxiv.org/abs/2507.16281 https://arxiv.org/pdf/2507…

The Bode Plots for Sliding-Mode Control Design
This paper develops a unified frequency-domain framework for the analysis of sliding-mode control systems, encompassing both discontinuous and Lipschitz-continuous implementations. Using describing function (DF) theory, closed-form expressions are derived for the amplitude and frequency of chattering oscillations, as well as equivalent gain (EG) models that enable closed-loop sensitivity analysis. The proposed methodology captures the influence of actuator dynamics, control parameters, and dist…

High resolution radio observations of the Chamaeleon star-forming region
Ernesto Garc\'ia Valencia, Laurent Loinard, Arnaud Belloche, Gisela N. Ortiz Le\'on, Sergio A. Dzib, Cherie K. Day, Roopesh Ojha
https://arxiv.org/abs/2506.15927

High resolution radio observations of the Chamaeleon star-forming region
We report on large-scale radio observations of the Chamaeleon star-forming region obtained with the Australia Telescope Compact Array (ATCA) that led to the definite detection of five young stars and the tentative detection of five more. As in other regions surveyed in the radio domain, the majority of detected sources are fairly evolved low-mass T Tauri stars, but we also detect one protostellar object (Ced 110 IRS4) and one Herbig Ae/Be star. With the exception of the protostellar source, the…

Addressing the ML Domain Adaptation Problem for Networking: Realistic and Controllable Training Data Generation with NetReplica
Jaber Daneshamooz, Jessica Nguyen, William Chen, Sanjay Chandrasekaran, Satyandra Guthula, Ankit Gupta, Arpit Gupta, Walter Willinger
https://arxiv.org/abs/2507.13476

Addressing the ML Domain Adaptation Problem for Networking: Realistic and Controllable Training Data Generation with NetReplica
Machine learning models in networking suffer from the domain adaptation problem; models trained in one domain often fail when deployed in different production environments. This paper presents the design and implementation of NetReplica, a system that addresses this challenge by generating training datasets with two critical properties: realism in protocol dynamics and controllability of network conditions. NetReplica models networks as collections of bottleneck links with specific attributes, …

MGT-Prism: Enhancing Domain Generalization for Machine-Generated Text Detection via Spectral Alignment
Shengchao Liu, Xiaoming Liu, Chengzhengxu Li, Zhaohan Zhang, Guoxin Ma, Yu Lan, Shuai Xiao
https://arxiv.org/abs/2508.13768

MGT-Prism: Enhancing Domain Generalization for Machine-Generated Text Detection via Spectral Alignment
Large Language Models have shown growing ability to generate fluent and coherent texts that are highly similar to the writing style of humans. Current detectors for Machine-Generated Text (MGT) perform well when they are trained and tested in the same domain but generalize poorly to unseen domains, due to domain shift between data from different sources. In this work, we propose MGT-Prism, an MGT detection method from the perspective of the frequency domain for better domain generalization. Our…

Engineering and exploiting self-driven domain wall motion in ferrimagnets for neuromorphic computing applications
Jeffrey A. Brock, Aleksandr Kurenkov, Ale\v{s} Hrabec, Laura J. Heyderman
https://arxiv.org/abs/2508.14252

Engineering and exploiting self-driven domain wall motion in ferrimagnets for neuromorphic computing applications
Magnetic domain wall motion has recently garnered significant interest as a physical mechanism to enable energy-efficient, next-generation brain-inspired computing architectures. However, realizing all behaviors required for neuromorphic computing within standard material systems remains a significant challenge, as these functionalities often rely on competing interactions. Here, we demonstrate how spontaneous domain wall motion in response to locally engineered lateral exchange coupling in tra…

Active, reactive and instantaneous optical forces on small particles in the time domain: Ultrafast attosecond subcycle pulses
Xiaohao Xu, Francisco J. Valdivia Valero, Manuel Nieto-Vesperinas
https://arxiv.org/abs/2506.15917

Active, reactive and instantaneous optical forces on small particles in the time domain: Ultrafast attosecond subcycle pulses
Recently discovered reactive optical forces have nule time-average of their instantaneous values on monochromatic illumination, so that their detection suggests the use of ultrafast optics, specially in the femto and attosecond domains. By using illumination with subcycle attosecond evanescent pulses, we report a theoretical study of the time variations of reactive forces versus those of active and instantaneous ones on small resonant particles that we consider dipolar; and show their correspon…

A Human-Centered Approach to Identifying Promises, Risks, & Challenges of Text-to-Image Generative AI in Radiology
Katelyn Morrison, Arpit Mathur, Aidan Bradshaw, Tom Wartmann, Steven Lundi, Afrooz Zandifar, Weichang Dai, Kayhan Batmanghelich, Motahhare Eslami, Adam Perer
https://arxiv.org/abs/2507.16207

A Human-Centered Approach to Identifying Promises, Risks, & Challenges of Text-to-Image Generative AI in Radiology
As text-to-image generative models rapidly improve, AI researchers are making significant advances in developing domain-specific models capable of generating complex medical imagery from text prompts. Despite this, these technical advancements have overlooked whether and how medical professionals would benefit from and use text-to-image generative AI (GenAI) in practice. By developing domain-specific GenAI without involving stakeholders, we risk the potential of building models that are either …

Multi-Domain Optimization Framework for ISAC: From Electromagnetic Shaping to Network Cooperation
Rang Liu, Ming Li, Mehdi Zafari, Bjorn Ottersten, A. Lee Swindlehurst
https://arxiv.org/abs/2506.16011

Multi-Domain Optimization Framework for ISAC: From Electromagnetic Shaping to Network Cooperation
Integrated sensing and communication (ISAC) has emerged as a key feature for sixth-generation (6G) networks, providing an opportunity to meet the dual demands of communication and sensing. Existing ISAC research primarily focuses on baseband optimization at individual access points, with limited attention to the roles of electromagnetic (EM) shaping and network-wide coordination. The intricate interdependencies between these domains remain insufficiently explored, leaving their full potential f…

Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with constraints
Zhenyun Yin, Shujie Wang, Xuhong Wang, Xingjun Ma, Yinchun Wang
https://arxiv.org/abs/2507.16727

Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with constraints
Improving the reliability of large language models (LLMs) is critical for deploying them in real-world scenarios. In this paper, we propose \textbf{Deliberative Searcher}, the first framework to integrate certainty calibration with retrieval-based search for open-domain question answering. The agent performs multi-step reflection and verification over Wikipedia data and is trained with a reinforcement learning algorithm that optimizes for accuracy under a soft reliability constraint. Empirical …

GRASPED: Graph Anomaly Detection using Autoencoder with Spectral Encoder and Decoder (Full Version)
Wei Herng Choong, Jixing Liu, Ching-Yu Kao, Philip Sperl
https://arxiv.org/abs/2508.15633

GRASPED: Graph Anomaly Detection using Autoencoder with Spectral Encoder and Decoder (Full Version)
Graph machine learning has been widely explored in various domains, such as community detection, transaction analysis, and recommendation systems. In these applications, anomaly detection plays an important role. Recently, studies have shown that anomalies on graphs induce spectral shifts. Some supervised methods have improved the utilization of such spectral domain information. However, they remain limited by the scarcity of labeled data due to the nature of anomalies. On the other hand, exist…

A variational approach to the emergence of domain structures in magnetostrictive solids
Marco Bresciani, Manuel Friedrich
https://arxiv.org/abs/2507.14091 …

A variational approach to the emergence of domain structures in magnetostrictive solids
We consider a variational model for magnetoelastic solids in the large-strain setting with the magnetization field defined on the unknown deformed configuration. Through a simultaneous linearization of the deformation and sharp-interface limit of the magnetization with respect to the easy axes, performed in terms of $Γ$-convergence, we identify a variational model describing the formation of domain structures and accounting for magnetostriction in the small-strain setting. Our analysis incorpo…

NPUEval: Optimizing NPU Kernels with LLMs and Open Source Compilers
Sarunas Kalade, Graham Schelle
https://arxiv.org/abs/2507.14403 https://

NPUEval: Optimizing NPU Kernels with LLMs and Open Source Compilers
Neural processing units (NPUs) are gaining prominence in power-sensitive devices like client devices, with AI PCs being defined by their inclusion of these specialized processors. Running AI workloads efficiently on these devices requires libraries of optimized kernels. Creating efficient kernels demands expertise in domain-specific C++ with vector intrinsics and in-depth knowledge of the target architecture. Unlike GPU programming, which has had years to mature, NPU programming is new, with sm…

SDDiff: Boost Radar Perception via Spatial-Doppler Diffusion
Shengpeng Wang, Xin Luo, Yulong Xie, Wei Wang
https://arxiv.org/abs/2506.16936 https://…

SDDiff: Boost Radar Perception via Spatial-Doppler Diffusion
Point cloud extraction (PCE) and ego velocity estimation (EVE) are key capabilities gaining attention in 3D radar perception. However, existing work typically treats these two tasks independently, which may neglect the interplay between radar's spatial and Doppler domain features, potentially introducing additional bias. In this paper, we observe an underlying correlation between 3D points and ego velocity, which offers reciprocal benefits for PCE and EVE. To fully unlock such inspiring potenti…

A Prior-Guided Joint Diffusion Model in Projection Domain for PET Tracer Conversion
Fang Chen, Weifeng Zhang, Xingyu Ai, BingXuan Li, An Li, Qiegen Liu
https://arxiv.org/abs/2506.16733

A Prior-Guided Joint Diffusion Model in Projection Domain for PET Tracer Conversion
Positron emission tomography (PET) is widely used to assess metabolic activity, but its application is limited by the availability of radiotracers. 18F-labeled fluorodeoxyglucose (18F-FDG) is the most commonly used tracer but shows limited effectiveness for certain tumors. In contrast, 6-18F-fluoro-3,4-dihydroxy-L-phenylalanine (18F-DOPA) offers higher specificity for neuroendocrine tumors and neurological disorders. However, its complex synthesis and limitations in transportation and clinical …

Heute vor 60 Jahren: Am 23. Juli 1965 zündeten die USA im Rahmen von Operation #Flintlock die 3. #Atombombe "Bronze". Flintlock war eine Serie von Tests zwischen 65/66 bei der insgesamt 47 #Kernwaffentests

Replaced article(s) found for hep-th. https://arxiv.org/list/hep-th/new
[2/2]:
- Creation of domain-wall Skyrmions in chiral magnets
Sven Bjarke Gudnason, Yuki Amari, Muneto Nitta

Optimizing Legal Document Retrieval in Vietnamese with Semi-Hard Negative Mining
Van-Hoang Le, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
https://arxiv.org/abs/2507.14619

Optimizing Legal Document Retrieval in Vietnamese with Semi-Hard Negative Mining
Large Language Models (LLMs) face significant challenges in specialized domains like law, where precision and domain-specific knowledge are critical. This paper presents a streamlined two-stage framework consisting of Retrieval and Re-ranking to enhance legal document retrieval efficiency and accuracy. Our approach employs a fine-tuned Bi-Encoder for rapid candidate retrieval, followed by a Cross-Encoder for precise re-ranking, both optimized through strategic negative example mining. Key innov…

Ahlfors-regularity for minimizers of a multiphase optimal design problem
Luca Esposito, Lorenzo Lamberti, Giovanni Pisante
https://arxiv.org/abs/2506.15861

Ahlfors-regularity for minimizers of a multiphase optimal design problem
We establish an Alhfors-regularity result for minimizers of a multiphase optimal design problem. It is a variant of the classical variational problem which involves a finite number of chambers $\mathcal{E}(i)$ of prescribed volume that partition a given domain $Ω\subset\mathbb{R}^n$. The cost functional associated with a configuration $\left(\{\mathcal{E}(i)\}_i,u\right)$ is made up of the perimeter of the partition interfaces and a Dirichlet energy term, which is discontinuous across the inte…

interactome_pdz: PDZ-domain interactome (2005)
A network of PDZ-domain-mediated protein–protein binding interactions, extracted from the PDZBase database. Nodes represent proteins and an edge represents a binding interaction between two proteins.
This network has 212 nodes and 244 edges.
Tags: Biological, Protein interactions, Unweighted

Graphical Analysis of Nonlinear Multivariable Feedback Systems
Julius P. J. Krebbekx, Roland T\'oth, Amritam Das
https://arxiv.org/abs/2507.16513 https…

Graphical Analysis of Nonlinear Multivariable Feedback Systems
Scaled Relative Graphs (SRGs) provide a novel graphical frequency-domain method for the analysis of nonlinear systems. There have been recent efforts to generalize SRG analysis to Multiple-Input Multiple-Output (MIMO) systems. However, these attempts yielded only results for square systems, and in some cases, only methods applicable for Linear Time-Invariant (LTI) systems. In this paper, we develop a complete SRG framework for the analysis of MIMO systems, which may be nonlinear and non-square.…

Continuous coupling of electron beam to a thin dielectric slab waveguide yields monochromatic Cherenkov diffraction radiation
Andrzej Szczepkowicz, Po-Wei Kuo, Yen-Cheh Huang
https://arxiv.org/abs/2508.15605

Continuous coupling of electron beam to a thin dielectric slab waveguide yields monochromatic Cherenkov diffraction radiation
A scheme for generation of monochromatic Cherenkov radiation in a thin dielectric layer is proposed. The electrons travel in vacuum parallel to a dielectric, exciting a single synchronous electromagnetic waveguide mode. The proposed scheme is studied quantitatively for near-infrared radiation in silicon induced by a 100-keV electron beam, using time-domain and frequency-domain numerical simulations, with material absorption and dispersion taken into account. This method of radiation generation …

Enhanced Dermatology Image Quality Assessment via Cross-Domain Training
Ignacio Hern\'andez Montilla, Alfonso Medela, Paola Pasquali, Andy Aguilar, Taig Mac Carthy, Gerardo Fern\'andez, Antonio Martorell, Enrique Onieva
https://arxiv.org/abs/2506.16116

Enhanced Dermatology Image Quality Assessment via Cross-Domain Training
Teledermatology has become a widely accepted communication method in daily clinical practice, enabling remote care while showing strong agreement with in-person visits. Poor image quality remains an unsolved problem in teledermatology and is a major concern to practitioners, as bad-quality images reduce the usefulness of the remote consultation process. However, research on Image Quality Assessment (IQA) in dermatology is sparse, and does not leverage the latest advances in non-dermatology IQA,…

After some 30 years of using monkey.org I'm going to change my primary email address. Anti-spam measures has finally made using it with Gmail untenable, too many messages are disappearing. My new plan is to give a different domain entirely over to Google Apps for email. I am sad about this, the monkey folks are great.
Email used to be an open medium. It has now been wrecked by spammers and captured by a few companies.

A New High-Performing Method for Solving the Homogeneous Teukolsky Equation
Ye Jiang, Wen-Biao Han
https://arxiv.org/abs/2507.15363 https://

A New High-Performing Method for Solving the Homogeneous Teukolsky Equation
The numerical waveforms for the extreme mass-ratio inspirals (EMRIs) require a huge amount of homogeneous solutions of the Teukolsky equation in the frequency domain. The calculation accuracy and efficiency of the homogeneous solutions are the key performance bottleneck in waveform generation. In this paper, we propose a new numerical method based on the analytical series expansion which is most efficient for computing the homogeneous solutions with very high accuracy and a wider frequency rang…

Expert-Guided LLM Reasoning for Battery Discovery: From AI-Driven Hypothesis to Synthesis and Characterization
Shengchao Liu, Hannan Xu, Yan Ai, Huanxin Li, Yoshua Bengio, Harry Guo
https://arxiv.org/abs/2507.16110

Expert-Guided LLM Reasoning for Battery Discovery: From AI-Driven Hypothesis to Synthesis and Characterization
Large language models (LLMs) leverage chain-of-thought (CoT) techniques to tackle complex problems, representing a transformative breakthrough in artificial intelligence (AI). However, their reasoning capabilities have primarily been demonstrated in solving math and coding problems, leaving their potential for domain-specific applications-such as battery discovery-largely unexplored. Inspired by the idea that reasoning mirrors a form of guided search, we introduce ChatBattery, a novel agentic f…

Observation of relativistic domain wall motion in amorphous ferrimagnets
Pietro Diona, Luca Maranzana, Sergey Artyukhin, Giacomo Sala
https://arxiv.org/abs/2508.13950 https://…

Observation of relativistic domain wall motion in amorphous ferrimagnets
Domain walls in magnetic materials are sine-Gordon solitons characterized by relativistic kinematics, with the maximum spin-wave group velocity setting a limit for the domain wall speed and hence the highest operation frequency of magnetic devices. This relativistic regime has been observed only in crystalline iron-garnet ferrimagnets but is in general expected in many magnetic materials. In particular, amorphous rare-earth -- transition metal ferrimagnets should exhibit relativistic effects be…

A System Level Compiler for Massively-Parallel, Spatial, Dataflow Architectures
Dirk Van Essendelft, Patrick Wingo, Terry Jordan, Ryan Smith, Wissam Saidi
https://arxiv.org/abs/2506.15875

A System Level Compiler for Massively-Parallel, Spatial, Dataflow Architectures
We have developed a novel compiler called the Multiple-Architecture Compiler for Advanced Computing Hardware (MACH) designed specifically for massively-parallel, spatial, dataflow architectures like the Wafer Scale Engine. Additionally, MACH can execute code on traditional unified-memory devices. MACH addresses the complexities in compiling for spatial architectures through a conceptual Virtual Machine, a flexible domain-specific language, and a compiler that can lower high-level languages to m…

The Emergence of Deep Reinforcement Learning for Path Planning
Thanh Thi Nguyen, Saeid Nahavandi, Imran Razzak, Dung Nguyen, Nhat Truong Pham, Quoc Viet Hung Nguyen
https://arxiv.org/abs/2507.15469

The Emergence of Deep Reinforcement Learning for Path Planning
The increasing demand for autonomous systems in complex and dynamic environments has driven significant research into intelligent path planning methodologies. For decades, graph-based search algorithms, linear programming techniques, and evolutionary computation methods have served as foundational approaches in this domain. Recently, deep reinforcement learning (DRL) has emerged as a powerful method for enabling autonomous agents to learn optimal navigation strategies through interaction with t…

Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching
Hyeonseo Lee, Juhyun Park, Jihyong Oh, Chanho Eom
https://arxiv.org/abs/2506.12413

Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching
Person Re-identification (ReID) aims to retrieve images of the same individual captured across non-overlapping camera views, making it a critical component of intelligent surveillance systems. Traditional ReID methods assume that the training and test domains share similar characteristics and primarily focus on learning discriminative features within a given domain. However, they often fail to generalize to unseen domains due to domain shifts caused by variations in viewpoint, background, and l…

Heute vor 60 Jahren: Am 22. Juli 1965 zündeten die USA im Rahmen von Operation #Flintlock die 2. #Atombombe "Pongee". Flintlock war eine Serie von Tests zwischen 65/66 bei der insgesamt 47 #Kernwaffentests

TracSum: A New Benchmark for Aspect-Based Summarization with Sentence-Level Traceability in Medical Domain
Bohao Chu, Meijie Li, Sameh Frihat, Chengyu Gu, Georg Lodde, Elisabeth Livingstone, Norbert Fuhr
https://arxiv.org/abs/2508.13798

TracSum: A New Benchmark for Aspect-Based Summarization with Sentence-Level Traceability in Medical Domain
While document summarization with LLMs has enhanced access to textual information, concerns about the factual accuracy of these summaries persist, especially in the medical domain. Tracing evidence from which summaries are derived enables users to assess their accuracy, thereby alleviating this concern. In this paper, we introduce TracSum, a novel benchmark for traceable, aspect-based summarization, in which generated summaries are paired with sentence-level citations, enabling users to trace b…

Enhancing patent retrieval using automated patent summarization
Eleni Kamateri, Renukswamy Chikkamath, Michail Salampasis, Linda Andersson, Markus Endres
https://arxiv.org/abs/2507.16371

Enhancing patent retrieval using automated patent summarization
Effective query formulation is a key challenge in long-document Information Retrieval (IR). This challenge is particularly acute in domain-specific contexts like patent retrieval, where documents are lengthy, linguistically complex, and encompass multiple interrelated technical topics. In this work, we present the application of recent extractive and abstractive summarization methods for generating concise, purpose-specific summaries of patent documents. We further assess the utility of these a…

A robust and stable phase field method for structural topology optimization
Huangxin Chen, Piaopiao Dong, Dong Wang, Xiao-Ping Wang
https://arxiv.org/abs/2507.16519

A robust and stable phase field method for structural topology optimization
This paper presents a novel phase-field-based methodology for solving minimum compliance problems in topology optimization under fixed external loads and body forces. The proposed framework characterizes the optimal structure through an order parameter function, analogous to phase-field models in materials science, where the design domain and its boundary are intrinsically represented by the order parameter function. The topology optimization problem is reformulated as a constrained minimizatio…

Holographic Baryons as Quantum Hall Droplets
Francesco Bigazzi, Aldo Lorenzo Cotrone, Andrea Olzi, Jean-Loup Raymond
https://arxiv.org/abs/2506.16205 https…

Holographic Baryons as Quantum Hall Droplets
We provide a first-principle construction of baryons as quantum Hall droplets in single-flavor holographic QCD. The baryons are described as charged D6-branes with a circular boundary on a flavor D8-brane in the Type IIA backgrounds dual to the confining and non-confining phases. The holographic description allows us to calculate precisely their properties, such as mass and size. We also consider other objects with baryonic charge, such as vortons, domain walls with holes, and "sandwich vortons…

Convergence analysis of a balancing domain decomposition method for an elliptic optimal control problem with HDG discretizations
Sijing Liu, Jinjin Zhang
https://arxiv.org/abs/2508.13997

Convergence analysis of a balancing domain decomposition method for an elliptic optimal control problem with HDG discretizations
In this work, a balancing domain decomposition by constraints (BDDC) algorithm is applied to the nonsymmetric positive definite linear system arising from the hybridizable discontinuous Galerkin (HDG) discretization of an elliptic distributed optimal control problem. Convergence analysis for the BDDC preconditioned generalized minimal residual (GMRES) solver demonstrates that, when the subdomain size is small enough, the algorithm is robust with respect to the regularization parameter, and the …

Scaled Relative Graph Analysis of General Interconnections of SISO Nonlinear Systems
Julius P. J. Krebbekx, Roland T\'oth, Amritam Das
https://arxiv.org/abs/2507.15564

Scaled Relative Graph Analysis of General Interconnections of SISO Nonlinear Systems
Scaled Relative Graphs (SRGs) provide a novel graphical frequency-domain method for the analysis of nonlinear systems. However, we show that the current SRG analysis suffers from a pitfall that limits its applicability in analyzing practical nonlinear systems. We overcome this pitfall by introducing a novel reformulation of the SRG of a linear time-invariant operator and combining the SRG with the Nyquist criterion. The result is a theorem that can be used to assess stability and $L_2$-gain per…

Domain-randomized deep learning for neuroimage analysis
Malte Hoffmann
https://arxiv.org/abs/2507.13458 https://arxiv.org/pdf/2507.13…

Domain-randomized deep learning for neuroimage analysis
Deep learning has revolutionized neuroimage analysis by delivering unprecedented speed and accuracy. However, the narrow scope of many training datasets constrains model robustness and generalizability. This challenge is particularly acute in magnetic resonance imaging (MRI), where image appearance varies widely across pulse sequences and scanner hardware. A recent domain-randomization strategy addresses the generalization problem by training deep neural networks on synthetic images with random…

Fine-Tuning Lowers Safety and Disrupts Evaluation Consistency
Kathleen C. Fraser, Hillary Dawkins, Isar Nejadgholi, Svetlana Kiritchenko
https://arxiv.org/abs/2506.17209

Fine-Tuning Lowers Safety and Disrupts Evaluation Consistency
Fine-tuning a general-purpose large language model (LLM) for a specific domain or task has become a routine procedure for ordinary users. However, fine-tuning is known to remove the safety alignment features of the model, even when the fine-tuning data does not contain any harmful content. We consider this to be a critical failure mode of LLMs due to the widespread uptake of fine-tuning, combined with the benign nature of the "attack". Most well-intentioned developers are likely unaware that th…

Improving OCR using internal document redundancy
Diego Belzarena, Seginus Mowlavi, Aitor Artola, Camilo Mari\~no, Marina Gardella, Ignacio Ram\'irez, Antoine Tadros, Roy He, Natalia Bottaioli, Boshra Rajaei, Gregory Randall, Jean-Michel Morel
https://arxiv.org/abs/2508.14557

Improving OCR using internal document redundancy
Current OCR systems are based on deep learning models trained on large amounts of data. Although they have shown some ability to generalize to unseen data, especially in detection tasks, they can struggle with recognizing low-quality data. This is particularly evident for printed documents, where intra-domain data variability is typically low, but inter-domain data variability is high. In that context, current OCR methods do not fully exploit each document's redundancy. We propose an unsupervis…

Heute vor 61 Jahren: Am 22. August 1964 zündeten die #USA im Rahmen von Operation Whetstone die Atombombe "Canvasback". Whetstone war eine Serie von #Kernwaffentests bei der 1964/65 insgesamt 46 Bomben größtenteils im Testgebiet in

FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering
Chanyeol Choi, Jihoon Kwon, Alejandro Lopez-Lira, Chaewoon Kim, Minjae Kim, Juneha Hwang, Jaeseon Ha, Hojun Choi, Suyeol Yun, Yongjin Kim, Yongjae Lee
https://arxiv.org/abs/2508.14052

FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering
Accurate information retrieval (IR) is critical in the financial domain, where investors must identify relevant information from large collections of documents. Traditional IR methods-whether sparse or dense-often fall short in retrieval accuracy, as it requires not only capturing semantic similarity but also performing fine-grained reasoning over document structure and domain-specific knowledge. Recent advances in large language models (LLMs) have opened up new opportunities for retrieval with…

LLaSO: A Foundational Framework for Reproducible Research in Large Language and Speech Model
Yirong Sun, Yizhong Geng, Peidong Wei, Yanjun Chen, Jinghan Yang, Rongfei Chen, Wei Zhang, Xiaoyu Shen
https://arxiv.org/abs/2508.15418

LLaSO: A Foundational Framework for Reproducible Research in Large Language and Speech Model
The development of Large Speech-Language Models (LSLMs) has been slowed by fragmented architectures and a lack of transparency, hindering the systematic comparison and reproducibility of research. Unlike in the vision-language domain, the LSLM field suffers from the common practice of releasing model weights without their corresponding training data and configurations. To address these critical gaps, we introduce LLaSO, the first fully open, end-to-end framework for large-scale speech-language …

Heute vor 60 Jahren: Am 23. Juli 1965 zündeten die #USA im Rahmen von Operation Whetstone die Atombombe "Links". Whetstone war eine Serie von #Kernwaffentests bei der 1964/65 insgesamt 46 Bomben größtenteils im Testgebiet in

DeepWriter: A Fact-Grounded Multimodal Writing Assistant Based On Offline Knowledge Base
Song Mao, Lejun Cheng, Pinlong Cai, Guohang Yan, Ding Wang, Botian Shi
https://arxiv.org/abs/2507.14189

DeepWriter: A Fact-Grounded Multimodal Writing Assistant Based On Offline Knowledge Base
Large Language Models (LLMs) have demonstrated remarkable capabilities in various applications. However, their use as writing assistants in specialized domains like finance, medicine, and law is often hampered by a lack of deep domain-specific knowledge and a tendency to hallucinate. Existing solutions, such as Retrieval-Augmented Generation (RAG), can suffer from inconsistency across multiple retrieval steps, while online search-based methods often degrade quality due to unreliable web content…

Human-in-Context: Unified Cross-Domain 3D Human Motion Modeling via In-Context Learning
Mengyuan Liu, Xinshun Wang, Zhongbin Fang, Deheng Ye, Xia Li, Tao Tang, Songtao Wu, Xiangtai Li, Ming-Hsuan Yang
https://arxiv.org/abs/2508.10897

Human-in-Context: Unified Cross-Domain 3D Human Motion Modeling via In-Context Learning
This paper aims to model 3D human motion across domains, where a single model is expected to handle multiple modalities, tasks, and datasets. Existing cross-domain models often rely on domain-specific components and multi-stage training, which limits their practicality and scalability. To overcome these challenges, we propose a new setting to train a unified cross-domain model through a single process, eliminating the need for domain-specific components and multi-stage training. We first introd…

CCL-XCoT: An Efficient Cross-Lingual Knowledge Transfer Method for Mitigating Hallucination Generation
Weihua Zheng, Roy Ka-Wei Lee, Zhengyuan Liu, Kui Wu, AiTi Aw, Bowei Zou
https://arxiv.org/abs/2507.14239

CCL-XCoT: An Efficient Cross-Lingual Knowledge Transfer Method for Mitigating Hallucination Generation
Multilingual Large Language Models(MLLMs) demonstrate strong generalization across languages, yet they remain prone to hallucinations, especially in low-resource languages, due to training data imbalances. These hallucinations, which include inaccurate or fabricated outputs, are particularly problematic in domain-specific generation tasks (Chataigner et al., 2024). To address this challenge, we propose CCL-XCoT(Curriculum-based Contrastive Learning-based Cross-lingual Chain-of-Thought), a two-s…

DeSamba: Decoupled Spectral Adaptive Framework for 3D Multi-Sequence MRI Lesion Classification
Dezhen Wang, Sheng Miao, Rongxin Chai, Jiufa Cui
https://arxiv.org/abs/2507.15487

DeSamba: Decoupled Spectral Adaptive Framework for 3D Multi-Sequence MRI Lesion Classification
Magnetic Resonance Imaging (MRI) sequences provide rich spatial and frequency domain information, which is crucial for accurate lesion classification in medical imaging. However, effectively integrating multi-sequence MRI data for robust 3D lesion classification remains a challenge. In this paper, we propose DeSamba (Decoupled Spectral Adaptive Network and Mamba-Based Model), a novel framework designed to extract decoupled representations and adaptively fuse spatial and spectral features for le…

Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models
Rithesh Murthy, Ming Zhu, Liangwei Yang, Jielin Qiu, Juntao Tan, Shelby Heinecke, Huan Wang, Caiming Xiong, Silvio Savarese
https://arxiv.org/abs/2507.14241

Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models
Large Language Models (LLMs) perform best with well-crafted prompts, yet prompt engineering remains manual, inconsistent, and inaccessible to non-experts. We introduce Promptomatix, an automatic prompt optimization framework that transforms natural language task descriptions into high-quality prompts without requiring manual tuning or domain expertise. Promptomatix supports both a lightweight meta-prompt-based optimizer and a DSPy-powered compiler, with modular design enabling future extension …

Trained Miniatures: Low cost, High Efficacy SLMs for Sales & Marketing
Ishaan Bhola, Mukunda NS, Sravanth Kurmala, Harsh Nandwani, Arihant Jain
https://arxiv.org/abs/2508.15617

Trained Miniatures: Low cost, High Efficacy SLMs for Sales & Marketing
Large language models (LLMs) excel in text generation; however, these creative elements require heavy computation and are accompanied by a steep cost. Especially for targeted applications such as sales and marketing outreach, these costs are far from feasible. This paper introduces the concept of "Trained Miniatures" - Small Language Models(SLMs) fine-tuned for specific, high-value applications, generating similar domain-specific responses for a fraction of the cost.